Program Overview
This advanced program empowers finance, risk, and analytics leaders to transform traditional financial operations using AI, machine learning, and predictive data modeling. Participants will explore how leading organizations use AI for accurate forecasting, proactive risk mitigation, and automated fraud detection, integrating these models into FP&A, treasury, and compliance functions. Through real datasets, interactive simulations, and global case studies, participants will learn to build data-driven frameworks that improve financial accuracy, governance, and strategic agility — redefining finance as an intelligent, insight-led function.
Features
- Apply AI/ML models for accurate financial forecasting and scenario planning
- Use predictive analytics for proactive risk and liquidity management
- Implement AI-driven fraud detection and compliance monitoring frameworks
- Design data-driven decision systems to enhance finance function efficiency and control
Target audiences
- Finance Professionals and Data Scientists
- FP&A Teams
- Risk & Compliance Professionals
- Business Analysts
Curriculum
- 6 Sections
- 34 Lessons
- 1 Day
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- The Rise of Data-Driven Finance6
- 1.1Evolution from descriptive to predictive & prescriptive finance
- 1.2Digital finance stack — ERP + BI + ML + RPA
- 1.3Role of data governance, automation, and real-time analytics
- 1.4Key enablers: Big Data, Cloud, APIs, and Generative AI in finance
- 1.5Finance 4.0 framework — agility, accuracy, and automation
- 1.6Quick diagnostic – Assess organization’s data maturity across FP&A, risk, and fraud functions
- AI in Financial Forecasting & Scenario Planning7
- 2.1Time-series forecasting (ARIMA, Prophet, LSTM)
- 2.2AI-based demand and revenue projections
- 2.3Rolling forecasts vs. static budgets
- 2.4Scenario and sensitivity modelling using ML insights
- 2.5AI-driven cost variance and working capital optimization
- 2.6Case Example: Unilever & Microsoft — AI-powered financial forecasting reducing variance error by 20%
- 2.7Exercise: Build a simple regression-based forecasting model in Excel/Python to predict quarterly revenue or expenses
- AI/ML in Financial Risk Analytics7
- 3.1Predictive risk scoring and anomaly detection
- 3.2Credit default prediction using supervised learning models
- 3.3Stress testing and Monte Carlo simulations
- 3.4Early warning signals for liquidity and exposure management
- 3.5Linking ESG data to financial risk modelling
- 3.6Example: JP Morgan & HDFC Bank — AI models for risk scoring and liquidity forecasting
- 3.7Exercise: Analyze risk datasets to flag potential exposure patterns and suggest mitigation actions
- AI in Fraud Detection & Compliance Monitoring7
- 4.1Transaction anomaly detection using clustering and NLP
- 4.2Outlier detection in payments and expense claims
- 4.3Continuous control monitoring (CCM) and forensic analytics
- 4.4Reinforcement learning for adaptive fraud models
- 4.5AI-based KYC, AML, and compliance monitoring systems
- 4.6Case Example: Mastercard’s AI-led fraud prevention and PayPal’s real-time anomaly systems
- 4.7Fraud detection Challenge — Identify outliers in sample payment data using AI pattern rules
- Building the Intelligent Finance Function5
- 5.1AI-augmented FP&A and cognitive decision support
- 5.2Human + Machine collaboration for financial insight generation
- 5.3Integrating ML pipelines into finance workflows
- 5.4Data visualization for storytelling — Power BI, Tableau, and GenAI dashboard
- 5.5Group activity – Design a “Smart Finance CoE” framework for their organization
- Action Planning & Digital Roadmap2
Instructor
